研究方向
统计模拟、辐射传输模拟、激光雷达遥感.
基本介绍
从事激光雷达遥感算法、一类分类算法/物种分布模型、三维辐射传输模型的开发和应用研究。已发表中英文论文40多篇，Google Scholar引用3855次。担任《遥感学报》第六届青年编委和Frontiers in Earth Science-Geoinformatics副主编。代表成果包括：
(1) 提出基于一类数据的建模方法PBL (presence and background learning)和PBLC (positive and background learning with constraints)；提出基于一类数据的模型评价指标Fpb/Fcpb和评价曲线PB-ROC/PR plots。适用于解决单类分类/二值分类中缺乏负样本的问题(如物种分布模拟、遥感影像单类识别等)。
(2) 提出基于激光雷达点云的单木分割算法，为开源软件R的激光雷达数据处理包lidR和商业软件LiDAR360采用，Google Scholar引用850次，获得2013年美国摄影测量与遥感协会(ASPRS)的Talbert Abrams Award。
(3) 提出基于激光雷达点云数据和高性能计算的三维辐射传输模型VBRT (voxel-based radiative transfer)，可模拟太阳辐射传输、遥感成像等过程。
学术网页
ResearchGate
https://www.researchgate.net/profile/Wenkai-Li-13
Google Scholor
https://scholar.google.com/citations?hl=en&user=s_TDQzIAAAAJ
Web of Science
https://webofscience.clarivate.cn/wos/author/record/AFM-7916-2022
教育背景
2008-2013：加州大学默塞德分校环境系统专业，博士
2005-2008：北京大学环境工程专业，硕士
2001-2005：中山大学环境科学专业，学士
工作经历
2014-现在：中山大学地理科学与规划学院，副教授
2013-2014：加州大学默塞德分校内华达研究所，助理研究员
代表论文
10. Li, W.*, Hu, X., Su, Y., Tao, S., Ma, Q., and Guo, Q., 2024. A new method for voxel-based modelling of three-dimensional forest scenes with integration of terrestrial and airborne LiDAR data. Methods in Ecology and Evolution, 15: 569–582.
09. Li, W.*, and Guo, Q., 2021. Plotting receiver operating characteristic and precision-recall curves from presence and background data. Ecology and Evolution, 11(15): 10192–10206.
08. Li, W.*, Guo, Q., and Elkan, C., 2021. One-class remote sensing classification from positive and unlabeled background data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 730–746.
07. Li, W.*, Guo, Q., Tao, S., and Su, Y., 2018. VBRT: a novel voxel-based radiative transfer model for heterogeneous three-dimensional forest scenes. Remote Sensing of Environment, 206: 318–335.
06. Li, W., and Guo, Q., 2014. A new accuracy assessment method for one-class remote sensing classification. IEEE Transactions on Geoscience and Remote Sensing, 52(8): 4621–4632.
05. Li, W., and Guo, Q., 2013. How to assess the prediction accuracy of species presence–absence models without absence data?. Ecography, 36(7): 788–799.
04. Li, W., Guo, Q., Jakubowski M.K, and Kelly, M., 2012. A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering & Remote Sensing, 78(1): 75–84.
03. Guo, Q., Li, W., Liu, Y., and Tong, D., 2011. Predicting potential distributions of geographic events using one-class data: concepts and methods. International Journal of Geographical Information Science, 25 (10): 1697–1715.
02. Li, W., Guo, Q., and Elkan, C., 2011. Can we model the probability of presence of species without absence data?. Ecography, 34(6): 1096–1105.
01. Li, W., Guo, Q., and Elkan, C., 2011. A positive and unlabeled learning algorithm for one-class classification of remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 49(2): 717–725.
学术报告
5. “Modeling of 3D Forests Using Lidar Data”, Theory and Methods of Land Surface Remote Sensing Inversion Summer School, 北京师范大学, 7/11/2022.
4. “地理空间分布模拟中的一类数据问题”, 第一届生态遥感新方法研讨班, 中国科学院植物研究所, 12/2/2022.
3. “基于激光雷达数据 (Lidar) 森林三维辐射传输模拟”, 第五期激光雷达森林生态应用培训班, 中国科学院植物研究所, 6/3/2019.
2. “基于激光雷达 (Lidar) 点云的树木切割”, 第一期激光雷达森林生态应用培训班, 中国科学院植物研究所, 6/19/2015.
1. “A New Accuracy Measure for One-Class Classification of Remote Sensing Data”, Association of American Geographers 2013 Annual Meeting, Los Angeles, CA, 4/9/2013. (2nd winner for Remote Sensing Specialty Group Student Honors Paper Competition)
获奖情况
2. Remote Sensing Specialty Group Student Honors Paper (2nd Place)，美国地理学家协会(AAG)，2013.
1. Talbert Abrams Award (2nd Honorable Mention)，美国摄影测量与遥感协会(ASPRS)，2013.
